Arlette Laguiller:does the mainstay of the French political far-left enjoy linguistic parity with her male counterparts?

While language use has been argued to reflect gender asymmetry, increasing parity has been evidenced in official settings (Holmes, 2000; Dister and Moreau, 2006). Our hypothesis is that the French national press has developed a norm of equal linguistic treatment of men and women. In a corpus of articles from Libération, Le Monde, and Le Figaro, we examine the treatment of Arlette Laguiller, the female leader of the French extreme-left 'Worker's Struggle' Party (Lutte Ouvrière), during the run-up to the 2007 presidential elections. How Laguiller is referred to and described in comparison with her male counterparts evidences no asymmetry. Breaches to parity are only found in the right-wing Figaro newspaper. The ideological distance between the newspaper and the candidate suggests that power struggles are a primary source of asymmetrical treatments. The discursive functions of such treatments can be understood through an investigation based on a portable corpus linguistics methodology for the measure of discrimination. © 2011 Elsevier B.V.

Think small. The construction of imagined tradition in German "Land"-magazines

This article is the first linguistic analysis of a new category of lifestyle magazines in the German speaking countries, based on methods of corpus linguistics and multimodal discourse analysis. Since the launch of the magazine LandLust in Germany in 2005, more than twenty publications of so called "land magazines" have appeared on the market, attracting millions of readers. Our research analyses land magazines as discursive events. We examine the specific combination of discourses land magazines are serving or creating by looking at the semiotic practices – writing and images – they manifest themselves by. Our results show that the magazine under scrutiny does not simply provide new forms of escapism but also positions itself politically in subtle ways as part of the traditional-conservative spectrum by reacting to metalinguistic discourses such as purism and feminist criticism.

A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences. © 2008. Licensed under the Creative Commons.